Ranking Facts for Explaining Answers to Elementary Science Questions

TL;DR

This paper introduces a support vector machine-based method for ranking facts to generate explanations for elementary science questions, emphasizing interpretability and competitive performance against BERT-based models.

Contribution

It presents a systematic comparison of preference ranking approaches, demonstrating a scalable, interpretable method that outperforms some BERT variants in fact explanation tasks.

Findings

Support vector machine ranking can effectively prioritize relevant facts.

Feature-rich models achieve interpretability and competitive accuracy.

The approach outperforms some BERT-based reranking models.

Abstract

In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question's answer by 'connecting the dots' across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5,000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm. The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions are: (1) a case study in which two preference ordering approaches are systematically compared; (2) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (3) the human-engineered features make it an interpretable machine learning model for the task.